Zobrazeno 1 - 7 of 7 pro vyhledávání: '"Mahdiyeh Fathi"'
1
Akademický článek
Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India
Autor: Armin Moghimi, Chiranjit Singha, Mahdiyeh Fathi, Saied Pirasteh, Ali Mohammadzadeh, Masood Varshosaz, Jian Huang, Huxiong Li
Publikováno v: Quaternary Science Advances, Vol 14, Iss , Pp 100187- (2024)
Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random
Externí odkaz: https://doaj.org/article/1134954b0ec0404e9a6665b0ad946912
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2
Akademický článek
Enhancing Corn Yield Prediction in Iowa: A Concatenate-Based 2D-CNN-BILSTM Model with Integration of Sentinel-1/2 and SoilGRIDs Data
Autor: Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi
Publikováno v: Environmental Sciences Proceedings, Vol 29, Iss 1, p 2 (2023)
Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food a
Externí odkaz: https://doaj.org/article/5571299a38404454a66da0428f92fe22
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3
Akademický článek
3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data
Autor: Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi
Publikováno v: Remote Sensing, Vol 15, Iss 23, p 5551 (2023)
Ensuring food security in precision agriculture requires early prediction of soybean yield at various scales within the United States (U.S.), ranging from international to local levels. Accurate yield estimation is essential in preventing famine by p
Externí odkaz: https://doaj.org/article/73363aebbfbc4874a6c4b9afa3f57118
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5
Akademický článek
3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data.
Autor: Fathi, Mahdiyeh1 (AUTHOR) mahdiyeh.fathi@ut.ac.ir, Shah-Hosseini, Reza1 (AUTHOR) rshahosseini@ut.ac.ir, Moghimi, Armin2 (AUTHOR) moghimi@lufi.uni-hannover.de
Publikováno v: Remote Sensing. Dec2023, Vol. 15 Issue 23, p5551. 20p.
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7
Akademický článek
AUTOMATIC CORN AND SOYBEAN MAPPING BASED ON DEEP LEARNING METHODS (CASE STUDY: HAMILTON, HARDIN, BOONE, STORY, DALLAS, POLK, AND JUSPER COUNTIES IN LOWA STATE).
Autor: Fathi, M., Shah-Hosseini, R.
Publikováno v: ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2022/2023, Vol. 10 Issue 4/W1, p187-193, 7p
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